Abstract
In this paper, we propose an evolutionary method to adjust class association rules from both global and local perspectives. We discover an interesting phenomena that the classification performance could be improved if we import some prior-knowledge, in the form of equations, to re-rank the association rules. We make use of Genetic Network Programming to automatically search the prior-knowledge. In addition to rank the rules globally, we also develop a feedback mechanism to adjust the rules locally, by giving some rewards to good rules and penalties to bad ones. The experimental results on UCI datasets show that the proposed method could improve the classification accuracies effectively.
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Yang, G., Wu, J., Mabu, S., Shimada, K., Hirasawa, K. (2010). Adjusting Class Association Rules from Global and Local Perspectives Based on Evolutionary Computation. In: Bi, Y., Williams, MA. (eds) Knowledge Science, Engineering and Management. KSEM 2010. Lecture Notes in Computer Science(), vol 6291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15280-1_27
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DOI: https://doi.org/10.1007/978-3-642-15280-1_27
Publisher Name: Springer, Berlin, Heidelberg
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